How to practice with .dtype and .shape in Numpy?

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     Numpy has attributes for arrays. These attributes describes the structure and the content. These are given below…

  • ·       .dtype
  • ·       .shape

.dtype:

              It means ‘data type’. It gives the information about the values to be stored in the array.

eg. Integers,floats,strings.

.shape:

              It provides the dimensions of the array. It may be of single dimensional, two dimensional or three dimensional.

Let us create python program to implement using Numpy.

Sample Python Numpy code for .dtype:

# Example 1: Integer array

Import numpy as np

s_int = np.array([11, 22, 33, 44])

print("Array Elements:", s_int)

print("Data type of the Array is:", s_int.dtype)

Output:

Array Elements: [11 22 33 44]

Data type of the Array is: int64

# Example 2: Float array

Import numpy as np

s_float = np.array([2.2, 3.8, 7.6])

print("Array Elements:", s_float)

print("Data type of the Array is:", s_float.dtype)

Output:

Array Elements: [2.2 3.8 7.6]

Data type of the Array is: float64

# Example 3: Mixed types (NumPy will upcast)

Import numpy as np

s_mixed = np.array([1, 2.5, 3, 7.8, 'a'])

print("Array Elements:", s_mixed)

print("Data type of the Array is:", s_mixed.dtype)

Output:

Array Elements: ['1' '2.5' '3' '7.8' 'a']

Data type of the Array is: <U32

Next one is about .shape

.shape:

              It describes about dimensions.

#Single dimensional array

import numpy as np

arr1 = np.array([11, 22, 33, 44, 55])

print("Shape:", arr1.shape) 

Output:

Shape: (5,)

#Two Dimensional array

import numpy as np

arr2 = np.array([[11, 22, 33], [44, 55, 66]])

print("Shape:", arr2.shape)

Output:

Shape: (2, 3)

#Three Dimensional array

import numpy as np

arr3 = np.array([[[11, 22, 12], [33, 44, 45]], [[55, 66, 56], [77, 88, 89]]])

print("Shape:", arr3.shape) 

Output:

Shape: (2, 2, 3)

Hope, this code is useful to you. It is very important to know about .dtype and .shape while starting python numpy code. Keep Coding!!!

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